{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import tensorflow as tf\n",
    "import glob\n",
    "import imageio\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np \n",
    "import os \n",
    "import PIL \n",
    "import time \n",
    "from IPython import display"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "tf.config.list_physical_devices('GPU')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "(train_images,train_labels),(_,_) = tf.keras.datasets.mnist.load_data(path='/DataSets/Mnist/mnist.npz')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "len(train_images)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "type(train_images[0])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "source": [
    "plt.imshow(train_images[0])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5bd036dc10>"
      ]
     },
     "metadata": {},
     "execution_count": 31
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
     "metadata": {
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    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "source": [
    "1e-4"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.0001"
      ]
     },
     "metadata": {},
     "execution_count": 32
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "train_images[0].shape"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "# 将图片形状由(_,28,28)转成(_,28,28,1)\r\n",
    "train_images = train_images.reshape(train_images.shape[0],28,28,1).astype('float32')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "train_images = (train_images-127.5)/127.5 # 归一化到[-1,1]之间"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "BUFFER_SIZE = 60000\r\n",
    "BATCH_SIZE = 256"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "len(train_dataset)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "235"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "\"\"\"创建生成器\n",
    "\n",
    "由维度100的随机向量通过 conv2dTranspose反卷积上采用的方式来拟合到_,28,28,1形状的向量中\n",
    "\"\"\"\n",
    "\n",
    "def generator_model():\n",
    "    model = tf.keras.Sequential()\n",
    "    model.add(tf.keras.layers.Dense(7*7*256,use_bias=False,input_shape=(100,))) # 技巧点，需要一开始明确想要多少层\n",
    "    model.add(tf.keras.layers.BatchNormalization())\n",
    "    model.add(tf.keras.layers.LeakyReLU())\n",
    "    model.add(tf.keras.layers.Reshape((7,7,256))) # 重新定义形状为7x7x256\n",
    "\n",
    "    model.add(tf.keras.layers.Conv2DTranspose(128,(5,5),strides=(1,1),padding='same',use_bias=False))  # shape: 7,7,128\n",
    "    model.add(tf.keras.layers.BatchNormalization())\n",
    "    model.add(tf.keras.layers.LeakyReLU())\n",
    "\n",
    "    model.add(tf.keras.layers.Conv2DTranspose(64,(5,5),strides=(2,2),padding='same',use_bias=False)) # shape:  14,14,64\n",
    "    model.add(tf.keras.layers.BatchNormalization())\n",
    "    model.add(tf.keras.layers.LeakyReLU())\n",
    "\n",
    "    # 注意最终输出只有一个1个神经元，与 _,28,28,1的图片\n",
    "    model.add(tf.keras.layers.Conv2DTranspose(1,(5,5),strides=(2,2),padding='same',use_bias=False)) # shape: 28,28,1\n",
    "    \n",
    "\n",
    "    return model\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "# 使用没有训练的生成模型来生成一张图片看看\n",
    "\n",
    "before_gmod = generator_model()\n",
    "noise = tf.random.normal([1,100]) # 1是作为batch_size 维\n",
    "before_img = before_gmod(noise,training=False)\n",
    "\n",
    "plt.imshow(before_img[0,:,:,0],cmap='gray')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5bd830d8b0>"
      ]
     },
     "metadata": {},
     "execution_count": 13
    },
    {
     "output_type": "display_data",
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "\"\"\"\n",
    "创建判别器\n",
    "\n",
    "判别器就是一个图片分类器，将图片数据输入，返回一个维度(batch_size维是默认第一维)的真假输出,\n",
    "\n",
    "输入图片数值\n",
    "输出判定结果\n",
    "\n",
    "\"\"\"\n",
    "def discriminator_model():\n",
    "    model = tf.keras.Sequential()\n",
    "    model.add(tf.keras.layers.Conv2D(64,(5,5),strides=(2,2),padding='same',input_shape=[28,28,1])) # 输入的shape是28,28,1,输出为 _,14,14,64\n",
    "    model.add(tf.keras.layers.LeakyReLU())\n",
    "    model.add(tf.keras.layers.Dropout(0.3))\n",
    "\n",
    "    model.add(tf.keras.layers.Conv2D(128,(5,5),strides=(2,2),padding='same')) # 输出shape为： _,7,7,128\n",
    "    model.add(tf.keras.layers.LeakyReLU())\n",
    "    model.add(tf.keras.layers.Dropout(0.3))\n",
    "\n",
    "    model.add(tf.keras.layers.Flatten())  # 打平所有维度\n",
    "    model.add(tf.keras.layers.Dense(1))   # 最终输出一个判定值\n",
    "\n",
    "    return model "
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "# 使用没有训练的判别器来对前面那张提前生成的图片进行判别测试\n",
    "\n",
    "before_dmod = discriminator_model()\n",
    "before_result = before_dmod(before_img)\n",
    "before_result"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(1, 1), dtype=float32, numpy=array([[0.00265991]], dtype=float32)>"
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "\"\"\"\n",
    "定义损失函数和优化器\n",
    "\"\"\"\n",
    "\n",
    "#通用交叉熵损失函数, 两个模型都没有经过最终激活，所以参赛from_logits=True\n",
    "\n",
    "cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)  # 因为两个模型最后输出都没有经过分类激活，所以True"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "source": [
    "# 判别器的损失函数： 很直白 .把真实图片传入判别器得到的结果去拟合1，把随机生成的图片传入判别器得到的结果去拟合0，\n",
    "#最后将两者得到的损失结果加起来，得到一个综合的损失. 得到的结果自然是越小越好。即两者判别器的结果损失差异很小说明判别器很吊\n",
    "# 所以记住： 判别器损失函数是一个综合损失！\n",
    "def discriminator_loss(real_d,gen_d):\n",
    "    \"\"\"\n",
    "    real_d: 真实图片经过判别器得到的结果\n",
    "    gen_d: 生成的图片经过判别器得到的结果\n",
    "    \"\"\"\n",
    "    read_loss = cross_entropy(tf.ones_like(real_d),real_d)\n",
    "    fake_loss = cross_entropy(tf.zeros_like(gen_d),gen_d)\n",
    "    total_loss = read_loss + fake_loss\n",
    "    return total_loss"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "source": [
    "# 生成器损失函数: 生成器的损失是基于它最终生成图片进入判别器后的结果，它的目的就是让它生成的图片经过判别器后得到的结果拟合1\n",
    "# 即生成的图片要越向真的\n",
    "def generator_loss(gen_d):\n",
    "    \"\"\"\n",
    "    gen_d: 生成器生成的图片经过判别器后得到的判别结果\n",
    "    \"\"\"\n",
    "    return cross_entropy(tf.ones_like(gen_d),gen_d)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "source": [
    "# 优化器： 注意这是两个网络，判别器和生成器的优化器是不同的\n",
    "\n",
    "generator_optimizer = tf.keras.optimizers.Adam(1e-4)\n",
    "discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "source": [
    "# 新建一个生成模型与判别模型用于训练\n",
    "gmod = generator_model()\n",
    "dmod = discriminator_model()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "source": [
    "# 保存检查点\n",
    "\n",
    "checkpoint_dir = './training_checkpoints'\n",
    "checkpoint_prefix = os.path.join(checkpoint_dir,'ckpt')\n",
    "checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,\n",
    "                                    discriminator_optimizer=discriminator_optimizer,\n",
    "                                    generator=gmod,\n",
    "                                    discriminator=dmod)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "source": [
    "\"\"\" \n",
    "训练设置\n",
    "\"\"\"\n",
    "\n",
    "EPOCHS = 500\n",
    "noise_dim = 100\n",
    "num_examples_to_generate = 16\n",
    "\n",
    "seed = tf.random.normal([num_examples_to_generate,noise_dim]) # 固定种子"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "source": [
    "# 自定义训练步骤与梯度更新算法\n",
    "# 注意： 一定要使用tf.function来包裹自定义计算梯度函数，否则无效\n",
    "\n",
    "@tf.function\n",
    "def train_step(images):\n",
    "    noise = tf.random.normal([BATCH_SIZE,noise_dim])\n",
    "\n",
    "    with tf.GradientTape() as gen_tape,tf.GradientTape() as disc_tape:\n",
    "        generated_images = gmod(noise,training=True)  # 生成器先生成图片\n",
    "        real_d = dmod(images,training=True)  # 判别器判别真实图片\n",
    "        gen_d = dmod(generated_images,training=True)  #判别器判别真实图片\n",
    "\n",
    "        gen_loss = generator_loss(gen_d)  # 求生成图片的损失\n",
    "        disc_loss = discriminator_loss(real_d,gen_d)  # 求判别器的损失\n",
    "\n",
    "    gradients_of_generator = gen_tape.gradient(gen_loss,gmod.trainable_variables) # 生成器损失函数反向传播求梯度\n",
    "    gradients_of_discriminator = disc_tape.gradient(disc_loss,dmod.trainable_variables)  # 判别器损失函数反向传播求梯度\n",
    "\n",
    "    generator_optimizer.apply_gradients(zip(gradients_of_generator,gmod.trainable_variables)) # 生成器反向传播使用使用优化器\n",
    "    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator,dmod.trainable_variables)) # 判别器反向传播使用优化器"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "source": [
    "# 生成与保存图片\n",
    "\n",
    "def generate_and_save_images(model,epoch,test_input):\n",
    "    \"\"\"\n",
    "    注意：此时training必须是False,别影响反向传播\n",
    "    \"\"\"\n",
    "    predictions = model(test_input,training=False)\n",
    "    fig = plt.figure(figsize=(4,4))\n",
    "    for i in range(predictions.shape[0]):\n",
    "        plt.subplot(4,4,i+1)\n",
    "        plt.imshow(predictions[i,:,:,0]*127.5+127.5,cmap='gray') # 这是对生成的图片取完值后提取的东西\n",
    "        plt.axis('off')\n",
    "    plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))\n",
    "    plt.show()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "source": [
    "# 定义训练函数\n",
    "\n",
    "def train(dataset,epochs):\n",
    "    for epoch in range(epochs):\n",
    "        start = time.time()\n",
    "        for image_batch in dataset:\n",
    "            train_step(image_batch)\n",
    "\n",
    "        display.clear_output(wait=True)\n",
    "        generate_and_save_images(gmod,epoch+1,seed)\n",
    "\n",
    "        # 每15个epoch保存一次模型\n",
    "        if (epoch+1)%50 == 0:\n",
    "            checkpoint.save(file_prefix=checkpoint_prefix)\n",
    "\n",
    "        print('epoch {} is {} sec'.format(epoch+1,time.time()-start))\n",
    "\n",
    "    \n",
    "    # 最后一个epoch结束后生成图片\n",
    "    display.clear_output(wait=True)\n",
    "    generate_and_save_images(gmod,epochs,seed)\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "source": [
    "train(train_dataset,EPOCHS)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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      "text/plain": [
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     "text": [
      "epoch 151 is 3.0621695518493652 sec\n"
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    },
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     "output_type": "error",
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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      "\u001b[0;32m<ipython-input-37-6418b105d7b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mEPOCHS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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      "\u001b[0;32m~/ENV/TF_env/lib/python3.8/site-packages/tensorflow/python/data/ops/iterator_ops.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    759\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__next__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    760\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 761\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    762\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOutOfRangeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    763\u001b[0m       \u001b[0;32mraise\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/ENV/TF_env/lib/python3.8/site-packages/tensorflow/python/data/ops/iterator_ops.py\u001b[0m in \u001b[0;36m_next_internal\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    742\u001b[0m     \u001b[0;31m# to communicate that there is no more data to iterate over.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    743\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecution_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSYNC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 744\u001b[0;31m       ret = gen_dataset_ops.iterator_get_next(\n\u001b[0m\u001b[1;32m    745\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterator_resource\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    746\u001b[0m           \u001b[0moutput_types\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flat_output_types\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
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